def decompress(self, tensor_compressed, shape):
tensor_compressed, = tensor_compressed
cupy_tensor = cupy.fromDlpack(to_dlpack(tensor_compressed))
sign = cupy_tensor > 127
exps = cupy.bitwise_and(cupy_tensor, 0b01111111)
floats = cupy.left_shift((exps + 18).astype(cupy.int32), 23).view(cupy.float32)
tensor_decompressed = cupy.where(sign, -floats, floats)
tensor_decompressed = cupy.multiply((exps >= 1).astype(cupy.float32), tensor_decompressed)
return from_dlpack(tensor_decompressed.toDlpack()).view(shape)
def bitwise_left_shift(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.left_shift <numpy.left_shift>`.
See its docstring for more information.
"""
if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
raise TypeError(
"Only integer dtypes are allowed in bitwise_left_shift")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
# Note: bitwise_left_shift is only defined for x2 nonnegative.
if np.any(x2._array < 0):
raise ValueError(
"bitwise_left_shift(x1, x2) is only defined for x2 >= 0")
return Array._new(np.left_shift(x1._array, x2._array))
def gradient_noise_3d(fx, fy, fz, ix, iy, iz, seed):
vi = (__BN_X_NOISE_GEN * ix) + (__BN_Y_NOISE_GEN * iy) + (
__BN_Z_NOISE_GEN * iz) + __BN_SEED_NOISE_GEN * seed
vi = cp.bitwise_and(vi, 0xffffffff)
vi = cp.bitwise_xor(vi, cp.right_shift(vi, __BN_SHIFT_NOISE_GEN))
vi = cp.bitwise_and(vi, 0xff)
vi_l2 = cp.left_shift(vi, 2)
xvGrad = g_randomVectors[vi_l2]
yvGrad = g_randomVectors[vi_l2 + 1]
zvGrad = g_randomVectors[vi_l2 + 2]
xvPoint = fx - ix
yvPoint = fy - iy
zvPoint = fz - iz
return ((xvGrad * xvPoint) + (yvGrad * yvPoint) +
(zvGrad * zvPoint)) * 2.12
def __rlshift__(self, other):
return cupy.left_shift(other, self)
def __lshift__(self, other):
return cupy.left_shift(self, other)
